The US National Science Foundation (NSF) has granted over $12.7 million to nine research teams to investigate the potential biotechnological uses of RNA. The projects aim to develop innovative RNA-based methods for cancer therapies, disease-resistant crops, and antiviral treatments, with a focus on collaboration between chemistry and biology experts.
The U.S. National Science Foundation (NSF) has awarded over $12.7 million to nine research teams to explore the potential biotechnological applications of ribonucleic acid (RNA). These teams, supported under the Molecular Foundations for Biotechnology (MFB) program, will receive between $1 million and $1.65 million each. The program, a collaborative effort with the National Institutes of Health’s National Human Genome Research Institute (NHGRI), aims to develop innovative RNA-based methods for applications such as cancer therapies, disease-resistant crops, and antiviral treatments.
David Berkowitz, Director of NSF’s Chemistry Division, noted that the research could lead to significant breakthroughs at the intersection of chemistry and biology. Carolyn Hutter, Director of the Division of Genome Sciences at NHGRI, highlighted the transformative potential of new RNA research tools and technologies.
The awarded projects will engage experts in chemistry, biology, physics, mathematical modeling, and machine learning, with opportunities for industry partnerships to commercialize the research. Additionally, the initiative will provide hands-on training for students and early-career researchers through mentorships, workshops, and internships, aiming to expand participation in the U.S. STEM workforce.
Awarded projects include efforts by institutions such as Stanford University, University of Maryland, University of Colorado Denver, Oregon State University, Vanderbilt University, New York University, Yale University, Weill Cornell Medicine, and the Massachusetts Institute of Technology. These projects focus on various aspects of RNA research, from mapping subcellular transcriptomes to modeling RNA structures using artificial intelligence.
